Author

Galym Kapyshev

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2025

Collocation point strategies in physics-informed neural networks: A comparative study on the Burgers' equation

This study examines the influence of six collocation point sampling strategies—random uniform, uniform grid, Latin hypercube sampling, Sobol sequence, staggered triangular lattice, and a hybrid combined approach—on the performance of PINNs applied to the one-dimensional Burgers’ equation.

Ruslan Krasnozhonov, M. Nurtas, Zh. M. Kadirbayeva et al. · AI@DTESI · 0 citations

2025

Comparison of neural network models for prediction cryptocurrency price volatility in trading pairs

A comparative evaluation of five state-of-the-art machine learning models, such as Temporal Fusion Transformer (TFT), Temporal Convolutional Network (TCN), XGBoost, Random Forest, and a CNN-LSTM hybrid, for forecasting cryptocurrency price volatility across major trading pairs shows that TFT achieves the lowest test loss and highest classification accuracy, outperforming other architectures.

S. Mukhanov, Kaisar Bekbolat, I. Bazarbekov et al. · AI@DTESI · 0 citations